Scientists at UC San Francisco have developed an AI method that predicts Alzheimer’s Disease up to seven years in advance by analyzing patient records. The AI, utilizing machine learning, demonstrated a 72% accuracy in early prediction, identifying high cholesterol and osteoporosis as significant predictors, especially for women. By connecting clinical data with genetic databases through tools like UCSF’s SPOKE, researchers unveiled genes linked to Alzheimer’s, offering new avenues for early diagnosis and understanding the interplay between different health conditions and Alzheimer’s risk. This approach holds promise for enhancing precision medicine in Alzheimer’s and potentially other complex diseases, paving the way for more proactive healthcare strategies.
Source NeuroScienceNews
